KV Cache Optimization Strategies for Scalable and Efficient LLM Inference

TL;DR

This paper systematically reviews and categorizes various KV cache optimization techniques for large language models, addressing memory and throughput bottlenecks in scalable inference across diverse deployment scenarios.

Contribution

It provides a comprehensive analysis of five main KV cache optimization strategies, their trade-offs, and practical deployment guidance for scalable LLM inference.

Findings

No single technique is best for all scenarios.

Adaptive, multi-stage optimization is promising for future work.

Empirical evaluation across multiple deployment scenarios.

Abstract

The key-value (KV) cache is a foundational optimization in Transformer-based large language models (LLMs), eliminating redundant recomputation of past token representations during autoregressive generation. However, its memory footprint scales linearly with context length, imposing critical bottlenecks on GPU memory capacity, memory bandwidth, and inference throughput as production LLMs push context windows from thousands to millions of tokens. Efficient KV cache management has thus become a first-order challenge for scalable LLM deployment. This paper provides a systematic review of recent KV cache optimization techniques, organizing them into five principal directions: cache eviction, cache compression, hybrid memory solutions, novel attention mechanisms, and combination strategies. For each category we analyze the underlying mechanisms, deployment trade-offs, and empirical performance across memory reduction, throughput, and model accuracy metrics. We further map techniques to seven practical deployment scenarios, including long-context single requests, high-throughput datacenter serving, edge devices, multi-turn conversations, and accuracy-critical reasoning, providing actionable guidance for practitioners selecting among competing approaches. Our analysis reveals that no single technique dominates across all settings; instead, the optimal strategy depends on context length, hardware constraints, and workload characteristics, pointing toward adaptive, multi-stage optimization pipelines as a promising direction for future research.